Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations32681
Missing cells0
Missing cells (%)0.0%
Duplicate rows12
Duplicate rows (%)< 0.1%
Total size in memory23.2 MiB
Average record size in memory745.9 B

Variable types

Categorical6
Text3
Numeric7
Boolean1

Alerts

Dataset has 12 (< 0.1%) duplicate rowsDuplicates
Actitudinal is highly overall correlated with Axiologica and 3 other fieldsHigh correlation
Asignatura is highly overall correlated with Intensidad_HorariaHigh correlation
Axiologica is highly overall correlated with Actitudinal and 1 other fieldsHigh correlation
Cognitiva is highly overall correlated with En_Riesgo and 3 other fieldsHigh correlation
En_Riesgo is highly overall correlated with Actitudinal and 4 other fieldsHigh correlation
Intensidad_Horaria is highly overall correlated with AsignaturaHigh correlation
Nivel is highly overall correlated with Cognitiva and 3 other fieldsHigh correlation
Procedimental is highly overall correlated with Actitudinal and 4 other fieldsHigh correlation
Resultado is highly overall correlated with Actitudinal and 5 other fieldsHigh correlation
Grupo is highly imbalanced (82.0%) Imbalance

Reproduction

Analysis started2025-04-20 04:10:29.068595
Analysis finished2025-04-20 04:10:41.260350
Duration12.19 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Sede
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
FUSAGASUGÁ
21064 
GIRARDOT
11617 

Length

Max length10
Median length10
Mean length9.289067
Min length8

Characters and Unicode

Total characters303576
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFUSAGASUGÁ
2nd rowFUSAGASUGÁ
3rd rowFUSAGASUGÁ
4th rowFUSAGASUGÁ
5th rowFUSAGASUGÁ

Common Values

ValueCountFrequency (%)
FUSAGASUGÁ 21064
64.5%
GIRARDOT 11617
35.5%

Length

2025-04-19T23:10:41.387947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T23:10:41.570196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
fusagasugá 21064
64.5%
girardot 11617
35.5%

Most occurring characters

ValueCountFrequency (%)
A 53745
17.7%
G 53745
17.7%
U 42128
13.9%
S 42128
13.9%
R 23234
7.7%
F 21064
 
6.9%
Á 21064
 
6.9%
I 11617
 
3.8%
D 11617
 
3.8%
O 11617
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 303576
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 53745
17.7%
G 53745
17.7%
U 42128
13.9%
S 42128
13.9%
R 23234
7.7%
F 21064
 
6.9%
Á 21064
 
6.9%
I 11617
 
3.8%
D 11617
 
3.8%
O 11617
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 303576
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 53745
17.7%
G 53745
17.7%
U 42128
13.9%
S 42128
13.9%
R 23234
7.7%
F 21064
 
6.9%
Á 21064
 
6.9%
I 11617
 
3.8%
D 11617
 
3.8%
O 11617
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 282512
93.1%
None 21064
 
6.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 53745
19.0%
G 53745
19.0%
U 42128
14.9%
S 42128
14.9%
R 23234
8.2%
F 21064
 
7.5%
I 11617
 
4.1%
D 11617
 
4.1%
O 11617
 
4.1%
T 11617
 
4.1%
None
ValueCountFrequency (%)
Á 21064
100.0%
Distinct536
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-04-19T23:10:41.936736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters2091584
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc
2nd row333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc
3rd row333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc
4th row333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc
5th row333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc
ValueCountFrequency (%)
67e962b5d7a5d8acf0bf8d3ad7b649731fb5b93a572701cb179e7ad34085913f 114
 
0.3%
ea2800df56fb0d136863b14274c1ee624f87a3b2ed4f526f12378bb008d5c9a5 114
 
0.3%
f9d935cfdd65b86ab48b8744e498b948f967135a90a5b745123143c179e71953 114
 
0.3%
ff141b8ae5959a35409bcc4e5d44b303a1981ac691a6fffe2fe29d716209226d 114
 
0.3%
9519826a81aaa636f1e893349c83c1039cf7d753d244486d09a92b34305c2d3c 114
 
0.3%
5b0b2f629983dcb33cf57fdd482995d078d564763f3aa7cbce449fa9f92aecd9 114
 
0.3%
ebf9e2f6852a5df378d9a51de531bdc5a23c21726d5351b2d45d57bb7f119c53 114
 
0.3%
b603b2fbee9c95020d8c2068d1bb49a31818e9041052e9daa822ce3171bd6e95 114
 
0.3%
6e2ed213a16fd82c2eaa6a7f60ac88355973c844033b2587d01a47428c5ff94a 114
 
0.3%
7c5a67f9d54e3428e44ef6eec5c5632fd35917f5c1a5ee2bf8b20e1c39af0051 110
 
0.3%
Other values (526) 31545
96.5%
2025-04-19T23:10:42.469923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 136757
 
6.5%
2 135528
 
6.5%
1 135189
 
6.5%
5 133547
 
6.4%
8 131789
 
6.3%
d 131781
 
6.3%
3 131534
 
6.3%
b 130369
 
6.2%
a 129871
 
6.2%
f 129690
 
6.2%
Other values (6) 765529
36.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1305241
62.4%
Lowercase Letter 786343
37.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 135528
10.4%
1 135189
10.4%
5 133547
10.2%
8 131789
10.1%
3 131534
10.1%
7 128514
9.8%
0 128446
9.8%
4 128157
9.8%
9 127486
9.8%
6 125051
9.6%
Lowercase Letter
ValueCountFrequency (%)
c 136757
17.4%
d 131781
16.8%
b 130369
16.6%
a 129871
16.5%
f 129690
16.5%
e 127875
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1305241
62.4%
Latin 786343
37.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 135528
10.4%
1 135189
10.4%
5 133547
10.2%
8 131789
10.1%
3 131534
10.1%
7 128514
9.8%
0 128446
9.8%
4 128157
9.8%
9 127486
9.8%
6 125051
9.6%
Latin
ValueCountFrequency (%)
c 136757
17.4%
d 131781
16.8%
b 130369
16.6%
a 129871
16.5%
f 129690
16.5%
e 127875
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2091584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 136757
 
6.5%
2 135528
 
6.5%
1 135189
 
6.5%
5 133547
 
6.4%
8 131789
 
6.3%
d 131781
 
6.3%
3 131534
 
6.3%
b 130369
 
6.2%
a 129871
 
6.2%
f 129690
 
6.2%
Other values (6) 765529
36.6%

Grado
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.218751
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size255.4 KiB
2025-04-19T23:10:42.668711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile10
Maximum11
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7541509
Coefficient of variation (CV)0.5277414
Kurtosis-0.87671845
Mean5.218751
Median Absolute Deviation (MAD)2
Skewness0.22799614
Sum170554
Variance7.5853473
MonotonicityNot monotonic
2025-04-19T23:10:42.877724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
6 4045
12.4%
4 3932
12.0%
3 3751
11.5%
7 3693
11.3%
5 3650
11.2%
2 3255
10.0%
1 3201
9.8%
8 2532
7.7%
10 1921
5.9%
9 1843
5.6%
ValueCountFrequency (%)
1 3201
9.8%
2 3255
10.0%
3 3751
11.5%
4 3932
12.0%
5 3650
11.2%
6 4045
12.4%
7 3693
11.3%
8 2532
7.7%
9 1843
5.6%
10 1921
5.9%
ValueCountFrequency (%)
11 858
 
2.6%
10 1921
5.9%
9 1843
5.6%
8 2532
7.7%
7 3693
11.3%
6 4045
12.4%
5 3650
11.2%
4 3932
12.0%
3 3751
11.5%
2 3255
10.0%

Grupo
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
A
31793 
B
 
888

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32681
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 31793
97.3%
B 888
 
2.7%

Length

2025-04-19T23:10:43.136779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T23:10:43.403897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 31793
97.3%
b 888
 
2.7%

Most occurring characters

ValueCountFrequency (%)
A 31793
97.3%
B 888
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 32681
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 31793
97.3%
B 888
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 32681
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 31793
97.3%
B 888
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32681
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 31793
97.3%
B 888
 
2.7%

Periodo
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
1
10804 
2
7382 
4
7280 
3
7215 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32681
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 10804
33.1%
2 7382
22.6%
4 7280
22.3%
3 7215
22.1%

Length

2025-04-19T23:10:43.553249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T23:10:43.766098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10804
33.1%
2 7382
22.6%
4 7280
22.3%
3 7215
22.1%

Most occurring characters

ValueCountFrequency (%)
1 10804
33.1%
2 7382
22.6%
4 7280
22.3%
3 7215
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32681
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10804
33.1%
2 7382
22.6%
4 7280
22.3%
3 7215
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common 32681
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10804
33.1%
2 7382
22.6%
4 7280
22.3%
3 7215
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32681
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10804
33.1%
2 7382
22.6%
4 7280
22.3%
3 7215
22.1%

Año
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2024
14697 
2023
14594 
2025
3390 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters130724
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2024 14697
45.0%
2023 14594
44.7%
2025 3390
 
10.4%

Length

2025-04-19T23:10:43.928925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T23:10:44.069804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2024 14697
45.0%
2023 14594
44.7%
2025 3390
 
10.4%

Most occurring characters

ValueCountFrequency (%)
2 65362
50.0%
0 32681
25.0%
4 14697
 
11.2%
3 14594
 
11.2%
5 3390
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 65362
50.0%
0 32681
25.0%
4 14697
 
11.2%
3 14594
 
11.2%
5 3390
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 130724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 65362
50.0%
0 32681
25.0%
4 14697
 
11.2%
3 14594
 
11.2%
5 3390
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 65362
50.0%
0 32681
25.0%
4 14697
 
11.2%
3 14594
 
11.2%
5 3390
 
2.6%

Intensidad_Horaria
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.734647
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size255.4 KiB
2025-04-19T23:10:44.302665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.503778
Coefficient of variation (CV)0.54989838
Kurtosis-0.22261305
Mean2.734647
Median Absolute Deviation (MAD)1
Skewness0.61171523
Sum89371
Variance2.2613482
MonotonicityNot monotonic
2025-04-19T23:10:44.480926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 9140
28.0%
3 7073
21.6%
4 6521
20.0%
2 6357
19.5%
5 1868
 
5.7%
6 1180
 
3.6%
7 542
 
1.7%
ValueCountFrequency (%)
1 9140
28.0%
2 6357
19.5%
3 7073
21.6%
4 6521
20.0%
5 1868
 
5.7%
6 1180
 
3.6%
7 542
 
1.7%
ValueCountFrequency (%)
7 542
 
1.7%
6 1180
 
3.6%
5 1868
 
5.7%
4 6521
20.0%
3 7073
21.6%
2 6357
19.5%
1 9140
28.0%

Asignatura
Categorical

High correlation 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
EDUCACIÓN FÍSICA
2719 
LECTURA CRÍTICA
2699 
CIENCIAS NATURALES
2692 
LENGUA CASTELLANA
2688 
CIENCIAS SOCIALES
2630 
Other values (22)
19253 

Length

Max length37
Median length24
Mean length17.179584
Min length5

Characters and Unicode

Total characters561446
Distinct characters28
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCIENCIAS NATURALES
2nd rowCIENCIAS SOCIALES
3rd rowMATEMÁTICAS
4th rowLENGUA CASTELLANA
5th rowINGLÉS

Common Values

ValueCountFrequency (%)
EDUCACIÓN FÍSICA 2719
 
8.3%
LECTURA CRÍTICA 2699
 
8.3%
CIENCIAS NATURALES 2692
 
8.2%
LENGUA CASTELLANA 2688
 
8.2%
CIENCIAS SOCIALES 2630
 
8.0%
MATEMÁTICAS 2627
 
8.0%
ARTES 2423
 
7.4%
TECNOLOGÍAS INFORMÁTICAS 1499
 
4.6%
INTEGRALIDAD 1492
 
4.6%
APRENDIZAJE BASADO EN PROYECTOS 1486
 
4.5%
Other values (17) 9726
29.8%

Length

2025-04-19T23:10:44.722692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ciencias 5383
 
8.0%
educación 2780
 
4.1%
física 2746
 
4.1%
integralidad 2700
 
4.0%
lectura 2699
 
4.0%
crítica 2699
 
4.0%
e 2693
 
4.0%
naturales 2692
 
4.0%
lengua 2688
 
4.0%
castellana 2688
 
4.0%
Other values (34) 37876
56.0%

Most occurring characters

ValueCountFrequency (%)
A 72537
12.9%
I 59683
10.6%
E 54617
9.7%
C 50299
9.0%
N 46283
 
8.2%
S 37013
 
6.6%
34963
 
6.2%
T 34201
 
6.1%
L 25861
 
4.6%
R 23839
 
4.2%
Other values (18) 122150
21.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 526483
93.8%
Space Separator 34963
 
6.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 72537
13.8%
I 59683
11.3%
E 54617
10.4%
C 50299
9.6%
N 46283
8.8%
S 37013
 
7.0%
T 34201
 
6.5%
L 25861
 
4.9%
R 23839
 
4.5%
O 21073
 
4.0%
Other values (17) 101077
19.2%
Space Separator
ValueCountFrequency (%)
34963
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 526483
93.8%
Common 34963
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 72537
13.8%
I 59683
11.3%
E 54617
10.4%
C 50299
9.6%
N 46283
8.8%
S 37013
 
7.0%
T 34201
 
6.5%
L 25861
 
4.9%
R 23839
 
4.5%
O 21073
 
4.0%
Other values (17) 101077
19.2%
Common
ValueCountFrequency (%)
34963
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 540310
96.2%
None 21136
 
3.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 72537
13.4%
I 59683
11.0%
E 54617
10.1%
C 50299
9.3%
N 46283
8.6%
S 37013
 
6.9%
34963
 
6.5%
T 34201
 
6.3%
L 25861
 
4.8%
R 23839
 
4.4%
Other values (13) 101014
18.7%
None
ValueCountFrequency (%)
Ó 7772
36.8%
Í 7551
35.7%
Á 4126
19.5%
É 1540
 
7.3%
Ç 147
 
0.7%

Cognitiva
Real number (ℝ)

High correlation 

Distinct86
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.997827
Minimum7
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size255.4 KiB
2025-04-19T23:10:44.998794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile66
Q179
median86
Q391
95-th percentile96
Maximum100
Range93
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.672838
Coefficient of variation (CV)0.11515581
Kurtosis3.1937921
Mean83.997827
Median Absolute Deviation (MAD)6
Skewness-1.2893509
Sum2745133
Variance93.563796
MonotonicityNot monotonic
2025-04-19T23:10:45.222327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 1894
 
5.8%
90 1730
 
5.3%
91 1649
 
5.0%
92 1587
 
4.9%
87 1564
 
4.8%
86 1449
 
4.4%
84 1381
 
4.2%
88 1375
 
4.2%
94 1352
 
4.1%
85 1352
 
4.1%
Other values (76) 17348
53.1%
ValueCountFrequency (%)
7 1
< 0.1%
8 2
< 0.1%
10 2
< 0.1%
11 1
< 0.1%
15 2
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
21 1
< 0.1%
22 2
< 0.1%
23 2
< 0.1%
ValueCountFrequency (%)
100 322
 
1.0%
99 350
 
1.1%
98 227
 
0.7%
97 568
 
1.7%
96 654
 
2.0%
95 863
2.6%
94 1352
4.1%
93 1132
3.5%
92 1587
4.9%
91 1649
5.0%

Procedimental
Real number (ℝ)

High correlation 

Distinct86
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.74655
Minimum7
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size255.4 KiB
2025-04-19T23:10:45.517772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile64
Q179
median86
Q391
95-th percentile96
Maximum100
Range93
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.10207
Coefficient of variation (CV)0.12062671
Kurtosis3.235828
Mean83.74655
Median Absolute Deviation (MAD)6
Skewness-1.3605783
Sum2736921
Variance102.05183
MonotonicityNot monotonic
2025-04-19T23:10:45.718261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 1793
 
5.5%
90 1758
 
5.4%
91 1707
 
5.2%
87 1648
 
5.0%
92 1615
 
4.9%
84 1410
 
4.3%
88 1402
 
4.3%
86 1392
 
4.3%
94 1329
 
4.1%
85 1326
 
4.1%
Other values (76) 17301
52.9%
ValueCountFrequency (%)
7 1
 
< 0.1%
8 3
< 0.1%
10 2
< 0.1%
12 1
 
< 0.1%
13 2
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
22 2
< 0.1%
23 1
 
< 0.1%
24 3
< 0.1%
ValueCountFrequency (%)
100 278
 
0.9%
99 294
 
0.9%
98 217
 
0.7%
97 635
 
1.9%
96 662
 
2.0%
95 942
2.9%
94 1329
4.1%
93 1096
3.4%
92 1615
4.9%
91 1707
5.2%

Actitudinal
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.142835
Minimum0
Maximum100
Zeros27
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size255.4 KiB
2025-04-19T23:10:45.902821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70
Q185
median90
Q395
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.0875573
Coefficient of variation (CV)0.10310035
Kurtosis11.828548
Mean88.142835
Median Absolute Deviation (MAD)5
Skewness-2.1680928
Sum2880596
Variance82.583698
MonotonicityNot monotonic
2025-04-19T23:10:46.102931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 7225
22.1%
95 3749
11.5%
80 3609
11.0%
100 2990
 
9.1%
85 2682
 
8.2%
88 1082
 
3.3%
87 958
 
2.9%
92 930
 
2.8%
70 894
 
2.7%
89 848
 
2.6%
Other values (60) 7714
23.6%
ValueCountFrequency (%)
0 27
0.1%
8 1
 
< 0.1%
10 4
 
< 0.1%
15 2
 
< 0.1%
20 5
 
< 0.1%
23 1
 
< 0.1%
25 3
 
< 0.1%
26 1
 
< 0.1%
30 13
< 0.1%
33 1
 
< 0.1%
ValueCountFrequency (%)
100 2990
9.1%
99 220
 
0.7%
98 616
 
1.9%
97 510
 
1.6%
96 589
 
1.8%
95 3749
11.5%
94 729
 
2.2%
93 848
 
2.6%
92 930
 
2.8%
91 378
 
1.2%

Axiologica
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.805147
Minimum0
Maximum100
Zeros23
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size255.4 KiB
2025-04-19T23:10:46.352905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75
Q186
median90
Q395
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.4450799
Coefficient of variation (CV)0.094037817
Kurtosis15.424995
Mean89.805147
Median Absolute Deviation (MAD)5
Skewness-2.4779653
Sum2934922
Variance71.319375
MonotonicityNot monotonic
2025-04-19T23:10:46.543718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 7673
23.5%
95 4643
14.2%
100 3897
11.9%
80 2662
 
8.1%
85 2017
 
6.2%
93 983
 
3.0%
92 966
 
3.0%
96 941
 
2.9%
88 926
 
2.8%
89 852
 
2.6%
Other values (57) 7121
21.8%
ValueCountFrequency (%)
0 23
0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 6
 
< 0.1%
19 1
 
< 0.1%
20 5
 
< 0.1%
25 3
 
< 0.1%
30 11
< 0.1%
34 1
 
< 0.1%
35 1
 
< 0.1%
ValueCountFrequency (%)
100 3897
11.9%
99 297
 
0.9%
98 798
 
2.4%
97 557
 
1.7%
96 941
 
2.9%
95 4643
14.2%
94 796
 
2.4%
93 983
 
3.0%
92 966
 
3.0%
91 434
 
1.3%
Distinct73
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2025-04-19T23:10:47.102971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length36
Median length26
Mean length22.414002
Min length17

Characters and Unicode

Total characters732512
Distinct characters42
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALEJANDRA LEÓN DICELIS
2nd rowKATHERINE CUBILLOS VÉLEZ
3rd rowTATIANA GONZÁLEZ MORENO
4th rowKATHERINE CUBILLOS VÉLEZ
5th rowALEJANDRA MORALES GARCÍA
ValueCountFrequency (%)
sánchez 3056
 
3.1%
alejandra 3016
 
3.1%
rodríguez 2498
 
2.5%
rincón 2092
 
2.1%
stivenson 2037
 
2.1%
najas 2037
 
2.1%
buitrago 1720
 
1.8%
steven 1720
 
1.8%
barrero 1720
 
1.8%
torres 1717
 
1.8%
Other values (156) 76430
78.0%
2025-04-19T23:10:47.668811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 106319
14.5%
65362
 
8.9%
R 62605
 
8.5%
E 56956
 
7.8%
N 56516
 
7.7%
O 50444
 
6.9%
I 43947
 
6.0%
L 41273
 
5.6%
S 39254
 
5.4%
T 32431
 
4.4%
Other values (32) 177405
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 667030
91.1%
Space Separator 65362
 
8.9%
Decimal Number 120
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 106319
15.9%
R 62605
 
9.4%
E 56956
 
8.5%
N 56516
 
8.5%
O 50444
 
7.6%
I 43947
 
6.6%
L 41273
 
6.2%
S 39254
 
5.9%
T 32431
 
4.9%
C 20429
 
3.1%
Other values (21) 156856
23.5%
Decimal Number
ValueCountFrequency (%)
1 40
33.3%
2 16
 
13.3%
5 8
 
6.7%
6 8
 
6.7%
7 8
 
6.7%
8 8
 
6.7%
9 8
 
6.7%
4 8
 
6.7%
0 8
 
6.7%
3 8
 
6.7%
Space Separator
ValueCountFrequency (%)
65362
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 667030
91.1%
Common 65482
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 106319
15.9%
R 62605
 
9.4%
E 56956
 
8.5%
N 56516
 
8.5%
O 50444
 
7.6%
I 43947
 
6.6%
L 41273
 
6.2%
S 39254
 
5.9%
T 32431
 
4.9%
C 20429
 
3.1%
Other values (21) 156856
23.5%
Common
ValueCountFrequency (%)
65362
99.8%
1 40
 
0.1%
2 16
 
< 0.1%
5 8
 
< 0.1%
6 8
 
< 0.1%
7 8
 
< 0.1%
8 8
 
< 0.1%
9 8
 
< 0.1%
4 8
 
< 0.1%
0 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 711913
97.2%
None 20599
 
2.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 106319
14.9%
65362
9.2%
R 62605
 
8.8%
E 56956
 
8.0%
N 56516
 
7.9%
O 50444
 
7.1%
I 43947
 
6.2%
L 41273
 
5.8%
S 39254
 
5.5%
T 32431
 
4.6%
Other values (26) 156806
22.0%
None
ValueCountFrequency (%)
Á 6799
33.0%
Ó 5984
29.0%
Í 4249
20.6%
É 2354
 
11.4%
Ñ 835
 
4.1%
Ì 378
 
1.8%

Resultado
Real number (ℝ)

High correlation 

Distinct86
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.429271
Minimum5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size255.4 KiB
2025-04-19T23:10:47.890322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile70
Q181
median87
Q391
95-th percentile96
Maximum100
Range95
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.2917073
Coefficient of variation (CV)0.097059324
Kurtosis5.7837481
Mean85.429271
Median Absolute Deviation (MAD)5
Skewness-1.5085957
Sum2791914
Variance68.75241
MonotonicityNot monotonic
2025-04-19T23:10:48.069302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 1949
 
6.0%
89 1945
 
6.0%
91 1793
 
5.5%
87 1763
 
5.4%
88 1732
 
5.3%
86 1724
 
5.3%
92 1630
 
5.0%
85 1609
 
4.9%
84 1525
 
4.7%
93 1439
 
4.4%
Other values (76) 15572
47.6%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 2
< 0.1%
9 1
 
< 0.1%
10 2
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
17 2
< 0.1%
18 3
< 0.1%
19 1
 
< 0.1%
20 4
< 0.1%
ValueCountFrequency (%)
100 217
 
0.7%
99 213
 
0.7%
98 345
 
1.1%
97 502
 
1.5%
96 730
2.2%
95 1110
3.4%
94 1346
4.1%
93 1439
4.4%
92 1630
5.0%
91 1793
5.5%

Nivel
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
ALTO
23340 
BÁSICO
5903 
SUPERIOR
3117 
BAJO
 
321

Length

Max length8
Median length4
Mean length4.7427557
Min length4

Characters and Unicode

Total characters154998
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUPERIOR
2nd rowALTO
3rd rowALTO
4th rowSUPERIOR
5th rowALTO

Common Values

ValueCountFrequency (%)
ALTO 23340
71.4%
BÁSICO 5903
 
18.1%
SUPERIOR 3117
 
9.5%
BAJO 321
 
1.0%

Length

2025-04-19T23:10:48.266985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T23:10:48.470688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
alto 23340
71.4%
básico 5903
 
18.1%
superior 3117
 
9.5%
bajo 321
 
1.0%

Most occurring characters

ValueCountFrequency (%)
O 32681
21.1%
A 23661
15.3%
L 23340
15.1%
T 23340
15.1%
S 9020
 
5.8%
I 9020
 
5.8%
R 6234
 
4.0%
B 6224
 
4.0%
Á 5903
 
3.8%
C 5903
 
3.8%
Other values (4) 9672
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 154998
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 32681
21.1%
A 23661
15.3%
L 23340
15.1%
T 23340
15.1%
S 9020
 
5.8%
I 9020
 
5.8%
R 6234
 
4.0%
B 6224
 
4.0%
Á 5903
 
3.8%
C 5903
 
3.8%
Other values (4) 9672
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 154998
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 32681
21.1%
A 23661
15.3%
L 23340
15.1%
T 23340
15.1%
S 9020
 
5.8%
I 9020
 
5.8%
R 6234
 
4.0%
B 6224
 
4.0%
Á 5903
 
3.8%
C 5903
 
3.8%
Other values (4) 9672
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 149095
96.2%
None 5903
 
3.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 32681
21.9%
A 23661
15.9%
L 23340
15.7%
T 23340
15.7%
S 9020
 
6.0%
I 9020
 
6.0%
R 6234
 
4.2%
B 6224
 
4.2%
C 5903
 
4.0%
U 3117
 
2.1%
Other values (3) 6555
 
4.4%
None
ValueCountFrequency (%)
Á 5903
100.0%
Distinct464
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2025-04-19T23:10:48.735447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters2091584
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
2nd row77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
3rd row77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
4th row77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
5th row77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabea
ValueCountFrequency (%)
5e6336ba4dc4061a9835b7ca9ff9f7cb3460be9cbfe7df778451f765c4ed8ac5 114
 
0.3%
22c7fb72bf379aa7c086a1f4a71a42828dfa0d1bca282a056b2b575cc18ec243 114
 
0.3%
087e955b29fb14a0939676839f94716a0d484c19c681e6a91ca658a1ed7527dd 114
 
0.3%
8a59db949fbfa3448b7baa9846cdff2716e16cf13a27482094b4e5c9da15d132 114
 
0.3%
3713ac9da3dce5378592ca1fdab4ab85430eb45f51e79b3407d67a38199ecf7c 114
 
0.3%
eff9626bca10271224098b63c922abe6698881efd47cadc3f22ec39a65353330 114
 
0.3%
884718eb1cb083208033f0496dc85f22cddd8456a43ad721e428862c6ed88a8a 114
 
0.3%
e04b0e98e8ed34aed94d0fd4607b104c1a0e95245d4d1d59457cb6e24b7a2cf5 114
 
0.3%
6972dfae4c538733dd215603f7fd81a43f9ac6b0396a56b08aa075b202bb0899 114
 
0.3%
76f410cd43a36a0e832f41a95d4106eb689a389970237347895d5d15831fec84 114
 
0.3%
Other values (454) 31541
96.5%
2025-04-19T23:10:49.219144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 134626
 
6.4%
2 134497
 
6.4%
6 133983
 
6.4%
b 133633
 
6.4%
3 132919
 
6.4%
0 131157
 
6.3%
a 130679
 
6.2%
4 130648
 
6.2%
c 130495
 
6.2%
1 129419
 
6.2%
Other values (6) 769528
36.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1309470
62.6%
Lowercase Letter 782114
37.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 134626
10.3%
2 134497
10.3%
6 133983
10.2%
3 132919
10.2%
0 131157
10.0%
4 130648
10.0%
1 129419
9.9%
5 128334
9.8%
8 128025
9.8%
7 125862
9.6%
Lowercase Letter
ValueCountFrequency (%)
b 133633
17.1%
a 130679
16.7%
c 130495
16.7%
e 129328
16.5%
f 129198
16.5%
d 128781
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1309470
62.6%
Latin 782114
37.4%

Most frequent character per script

Common
ValueCountFrequency (%)
9 134626
10.3%
2 134497
10.3%
6 133983
10.2%
3 132919
10.2%
0 131157
10.0%
4 130648
10.0%
1 129419
9.9%
5 128334
9.8%
8 128025
9.8%
7 125862
9.6%
Latin
ValueCountFrequency (%)
b 133633
17.1%
a 130679
16.7%
c 130495
16.7%
e 129328
16.5%
f 129198
16.5%
d 128781
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2091584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 134626
 
6.4%
2 134497
 
6.4%
6 133983
 
6.4%
b 133633
 
6.4%
3 132919
 
6.4%
0 131157
 
6.3%
a 130679
 
6.2%
4 130648
 
6.2%
c 130495
 
6.2%
1 129419
 
6.2%
Other values (6) 769528
36.8%

En_Riesgo
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.0 KiB
False
26457 
True
6224 
ValueCountFrequency (%)
False 26457
81.0%
True 6224
 
19.0%
2025-04-19T23:10:49.385786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2025-04-19T23:10:39.515543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:33.269986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:34.436493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:35.414299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:36.537685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:37.434181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:38.358463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:39.641535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:33.421119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:34.554348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:35.549764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:36.670922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:37.554076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:38.489500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:39.776063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:33.621667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:34.703137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:35.733685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:36.790709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:37.681363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:38.619312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:39.919643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:33.743038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:34.837695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:35.953474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:36.919438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:37.825689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:38.742767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:40.156636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:33.996442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:34.971119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:36.107919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:37.041694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:37.952928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:38.864388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:40.281874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:34.183758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:35.097892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:36.281854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:37.178832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:38.085925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:39.221587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:40.438043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:34.315767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:35.221012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:36.404201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:37.305111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:38.219421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-19T23:10:39.363746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-19T23:10:49.495085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ActitudinalAsignaturaAxiologicaAñoCognitivaEn_RiesgoGradoGrupoIntensidad_HorariaNivelPeriodoProcedimentalResultadoSede
Actitudinal1.0000.1140.7180.0610.4950.520-0.1370.045-0.0150.4650.0310.5100.6760.080
Asignatura0.1141.0000.0790.4220.1700.2930.2280.1110.7230.2160.0900.1610.1420.126
Axiologica0.7180.0791.0000.0810.3840.452-0.1180.0600.0050.4240.0340.3940.5700.122
Año0.0610.4220.0811.0000.0340.0250.1550.1860.1340.0360.3420.0340.0400.045
Cognitiva0.4950.1700.3840.0341.0000.790-0.1440.054-0.3340.6720.0390.9300.9450.089
En_Riesgo0.5200.2930.4520.0250.7901.0000.1090.0270.2271.0000.0290.8140.8440.008
Grado-0.1370.228-0.1180.155-0.1440.1091.0000.3220.0230.0970.027-0.125-0.1510.300
Grupo0.0450.1110.0600.1860.0540.0270.3221.0000.0480.0420.0080.0460.0520.124
Intensidad_Horaria-0.0150.7230.0050.134-0.3340.2270.0230.0481.0000.1540.041-0.313-0.2720.046
Nivel0.4650.2160.4240.0360.6721.0000.0970.0420.1541.0000.0400.6750.7330.098
Periodo0.0310.0900.0340.3420.0390.0290.0270.0080.0410.0401.0000.0390.0400.000
Procedimental0.5100.1610.3940.0340.9300.814-0.1250.046-0.3130.6750.0391.0000.9520.106
Resultado0.6760.1420.5700.0400.9450.844-0.1510.052-0.2720.7330.0400.9521.0000.093
Sede0.0800.1260.1220.0450.0890.0080.3000.1240.0460.0980.0000.1060.0931.000

Missing values

2025-04-19T23:10:40.624490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-19T23:10:41.037615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SedeEstudianteGradoGrupoPeriodoAñoIntensidad_HorariaAsignaturaCognitivaProcedimentalActitudinalAxiologicaDocenteResultadoNivelIdentificacionEn_Riesgo
0FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320233CIENCIAS NATURALES95969394ALEJANDRA LEÓN DICELIS95SUPERIOR77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabeaFalse
1FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320233CIENCIAS SOCIALES94949595KATHERINE CUBILLOS VÉLEZ94ALTO77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabeaFalse
2FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320235MATEMÁTICAS74739595TATIANA GONZÁLEZ MORENO81ALTO77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabeaFalse
3FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320234LENGUA CASTELLANA97979595KATHERINE CUBILLOS VÉLEZ96SUPERIOR77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabeaFalse
4FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320234INGLÉS85869090ALEJANDRA MORALES GARCÍA87ALTO77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabeaFalse
5FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320232CREATIVIDAD E INNOVACIÓN94949095SANDRA SANTISTEBAN OSTOS94ALTO77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabeaFalse
6FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320234APRENDIZAJE BASADO EN PROYECTOS94949491ALEJANDRA LEÓN DICELIS93ALTO77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabeaFalse
7FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320233EDUCACIÓN FÍSICA979710080STEVEN BARRERO BUITRAGO95SUPERIOR77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabeaFalse
8FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320232LECTURA CRÍTICA92939598YENNY SOTELO GÓMEZ93ALTO77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabeaFalse
9FUSAGASUGÁ333f3585f373ea9e63004097032d9f5e21f43ba06ae185c24cd1d34309c28bcc1A320231ARTES92929090SANDRA SANTISTEBAN OSTOS92ALTO77467efb2ca484cbdd90ca871596ada0efa1b81ca9be838ca7992df0c48dabeaFalse
SedeEstudianteGradoGrupoPeriodoAñoIntensidad_HorariaAsignaturaCognitivaProcedimentalActitudinalAxiologicaDocenteResultadoNivelIdentificacionEn_Riesgo
32671GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120256MATEMÁTICAS77809090XIMENA VILLANUEVA ROJAS82ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17beaFalse
32672GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120254LENGUA CASTELLANA81818787VALENTINA SARABIA VARGAS83ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17beaFalse
32673GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120252LECTURA CRÍTICA82829090VALENTINA SARABIA VARGAS84ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17beaFalse
32674GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120256ENGLISH717485100ALEXANDER VARGAS GÓMEZ79BÁSICOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17beaTrue
32675GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120253EDUCACIÓN FÍSICA88848395FELIPE SÁNCHEZ SALDARRIAGA87ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17beaFalse
32676GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120253INNOVACIÓN Y EMPRENDIMIENTO84858585LILIANA SARABIA VARGAS84ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17beaFalse
32677GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120252APRENDIZAJE BASADO EN INVESTIGACIÓN83838586XIMENA VILLANUEVA ROJAS84ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17beaFalse
32678GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120252CENTRO DE INTERÉS ARTÍSTICO94929495CENTRO DE INTERÉS94ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17beaFalse
32679GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120251TECNOLOGÍAS INFORMÁTICAS84838080LILIANA SARABIA VARGAS82ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17beaFalse
32680GIRARDOT94f5f93868edf72f3f1c812de64b2950b84ca378b1a7c9297cff95b3e22b0c389A120251INTEGRALIDAD95959090STIVEN RUBIANO CAPADOR94ALTOc5e68cbe701e21016b0713e7290e36421fe4a34f373ee9b3325aa9754ce17beaFalse

Duplicate rows

Most frequently occurring

SedeEstudianteGradoGrupoPeriodoAñoIntensidad_HorariaAsignaturaCognitivaProcedimentalActitudinalAxiologicaDocenteResultadoNivelIdentificacionEn_Riesgo# duplicates
0GIRARDOT5f753840b6675e34d49b8601b82cd03c8c8200ddd94c8735363faa9787f2d1715A420241ARTES92919092FELIPE SÁNCHEZ SALDARRIAGA92ALTOb49bef9f68dfe219fac5688588e3c5ae4fc2f953491bb70a41011acac0ef539eFalse2
1GIRARDOT5f753840b6675e34d49b8601b82cd03c8c8200ddd94c8735363faa9787f2d1715A420241CREATIVIDAD E INNOVACIÓN83849595CAROLINA CORTÉS MENDOZA87ALTOb49bef9f68dfe219fac5688588e3c5ae4fc2f953491bb70a41011acac0ef539eFalse2
2GIRARDOT5f753840b6675e34d49b8601b82cd03c8c8200ddd94c8735363faa9787f2d1715A420241INTEGRALIDAD79598585CAMILO BONILLA TORRES74BÁSICOb49bef9f68dfe219fac5688588e3c5ae4fc2f953491bb70a41011acac0ef539eTrue2
3GIRARDOT5f753840b6675e34d49b8601b82cd03c8c8200ddd94c8735363faa9787f2d1715A420241TECNOLOGÍAS INFORMÁTICAS9292100100GUILLERMO MARIOTTE PARRA94ALTOb49bef9f68dfe219fac5688588e3c5ae4fc2f953491bb70a41011acac0ef539eFalse2
4GIRARDOT5f753840b6675e34d49b8601b82cd03c8c8200ddd94c8735363faa9787f2d1715A420242APRENDIZAJE BASADO EN PROYECTOS90898895FELIPE SÁNCHEZ SALDARRIAGA90ALTOb49bef9f68dfe219fac5688588e3c5ae4fc2f953491bb70a41011acac0ef539eFalse2
5GIRARDOT5f753840b6675e34d49b8601b82cd03c8c8200ddd94c8735363faa9787f2d1715A420242LECTURA CRÍTICA79798585VALENTINA SARABIA VARGAS80ALTOb49bef9f68dfe219fac5688588e3c5ae4fc2f953491bb70a41011acac0ef539eFalse2
6GIRARDOT5f753840b6675e34d49b8601b82cd03c8c8200ddd94c8735363faa9787f2d1715A420243CIENCIAS SOCIALES81819093SÁNCHEZ CANTOR ANAVE85ALTOb49bef9f68dfe219fac5688588e3c5ae4fc2f953491bb70a41011acac0ef539eFalse2
7GIRARDOT5f753840b6675e34d49b8601b82cd03c8c8200ddd94c8735363faa9787f2d1715A420243EDUCACIÓN FÍSICA91919193FELIPE SÁNCHEZ SALDARRIAGA91ALTOb49bef9f68dfe219fac5688588e3c5ae4fc2f953491bb70a41011acac0ef539eFalse2
8GIRARDOT5f753840b6675e34d49b8601b82cd03c8c8200ddd94c8735363faa9787f2d1715A420243LENGUA CASTELLANA85858383VALENTINA SARABIA VARGAS84ALTOb49bef9f68dfe219fac5688588e3c5ae4fc2f953491bb70a41011acac0ef539eFalse2
9GIRARDOT5f753840b6675e34d49b8601b82cd03c8c8200ddd94c8735363faa9787f2d1715A420244CIENCIAS NATURALES84898080ALBERTO REYES VILLANUEVA85ALTOb49bef9f68dfe219fac5688588e3c5ae4fc2f953491bb70a41011acac0ef539eFalse2